Langfuse vs LangSmith
Detailed side-by-side comparison to help you choose the right tool
Langfuse
🔴DeveloperAnalytics & Monitoring
Open-source LLM engineering platform for traces, prompts, and metrics.
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FreeLangSmith
🔴DeveloperAnalytics & Monitoring
Tracing, evaluation, and observability for LLM apps and agents.
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FreeFeature Comparison
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Langfuse - Pros & Cons
Pros
- ✓Fully open-source with self-hosting that has complete feature parity with the cloud version
- ✓Hierarchical tracing captures the full execution tree of complex agent workflows, not just LLM calls
- ✓Prompt management with versioning and production linking creates a tight iteration feedback loop
- ✓Native integrations with LangChain, LlamaIndex, OpenAI SDK, and Vercel AI SDK require minimal code changes
- ✓Evaluation system supports both automated LLM-as-judge scoring and human annotation queues
Cons
- ✗Dashboard analytics are functional but less polished than commercial observability platforms for executive reporting
- ✗UI performance degrades noticeably with very large trace volumes (millions of traces)
- ✗ClickHouse dependency for self-hosting adds operational complexity compared to PostgreSQL-only setups
- ✗Documentation can lag behind feature releases, especially for newer evaluation and dataset features
LangSmith - Pros & Cons
Pros
- ✓Best-in-class LLM tracing and debugging platform
- ✓Deep integration with LangChain ecosystem
- ✓Powerful evaluation and testing workflows for prompt development
- ✓Dataset management for building evaluation harnesses
- ✓Visual trace viewer makes debugging complex chains intuitive
Cons
- ✗Most valuable when used with LangChain — less useful standalone
- ✗Paid plans required for team features and higher volume
- ✗Data sent to LangSmith's servers — privacy considerations
- ✗Can add overhead to development workflow
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